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Abstract: A Machine Learning Approach to Quantifying the Error of Airborne Radio Occultation in Atmospheric Rivers (AGU25)

A Machine Learning Approach to Quantifying the Error of Airborne Radio Occultation in Atmospheric Rivers

Noah Barton, Scripps Institution of Oceanography, La Jolla, United States, Jennifer S Haase, Scripps Institution of Oceanography, UC San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States and Bing Cao, Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
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